Several systems and methods can be implemented to monitor mass flow of solid particles, such as grain, through a combine. Monitoring mass flow is useful for yield monitoring and evaluating harvesting areas, among many other uses.
Flow sensors are typically grouped into volumetric flow sensors, mass flow sensors, and indirect measurement devices. Volumetric flow sensors include paddlewheels and optical devices mounted in a clean grain elevator. Mass flow measurement devices include impact sensors, torque sensors mounted on elevator paddles, torque sensors mounted on the clean grain elevator drive shaft, diaphragm impact sensors, pivoted-auger-fed devices, and scales for weighing of the clean grain auger. Indirect methods of flow sensing include use of capacitive sensors, ultrasonic sensors, and x-ray measurement principles.
Many state-of-the-art commercial implementations rely on impact-type sensors and empirical relations between measured, time-averaged values of the impact force and the corresponding time-averaged rates of mass flow over typical sample periods. For example, a method for measuring mass flow rate in grain combines during harvesting employs impact mass flow sensors mounted in the clean grain elevators of the combines. Systems using mass flow sensors rely on the direct interaction of grains with an impact plate to accomplish mass flow rate estimation. An example impact sensor includes an impact plate and a force transducer that converts the time-averaged impact force into a voltage signal. Such impact-type sensors can be relatively simple structures that allow for independent operation and reduced risk for material build-up.
An example impact-based mass-flow sensor system 20 for a combine 22 is shown in FIGS. 1-2. Portions of FIGS. 1-2 are reprinted with permission from Reinke, R., Dankowicz, H., Phelan, J., Kang, W., (2011), Precision Agriculture 12:732-749, © Springer Science+Business Media, LLC, 2011. In the example system 20, disposed within a clean grain elevator, elevator paddles 24 disposed within a housing 25 are filled with solid particles (e.g., grain particles) 26 that enter via an input such as an auger 27 (shown in FIG. 1) that provides an input path to the system 20. The elevator paddles 24, disposed in the input path to receive the particles, are attached to a chain 28 that is cycled by a rotating sprocket 29 (shown in FIG. 2). As the paddles 24 move in an angular fashion, e.g., rotate around the sprocket 29, the solid particles 26 are propelled towards an impact plate 30. As momentum is lost in the subsequent collision between a plurality of the solid particles 26 (the amount of which may be equal to or less than all of the particles that enter the system 20) and the impact plate 30, an effective force is measured on the impact plate, due to internal deformations in the sensor, as a change in voltage of a force transducer 32. The signal from the force transducer 32 is sent to a processor 34 coupled to the force transducer via suitable signal (e.g., electrical) connections, such that the signal represents a measured force on the impact plate 30. This measured force, time-averaged over some typical sample period, along with knowledge of the dynamics of the system 20, allows for the corresponding time-averaged mass flow rate of the solid particles 26 to be estimated.
Typically, curve-fit schemes are used to characterize the relation between the time-averaged impact force and the time-averaged rate of particle mass flow during typical sample periods. During calibration, the relationship between the time-averaged impact force and the time-averaged rate of particle mass flow can be updated. However, such curve-fit schemes are highly dependent on the conditions at which calibration is performed. For instance, significant errors can result in estimating time-averaged mass flow rates at a certain threshold above calibration flow rates, and increased errors can also occur for low flow rates.
Simple linear models have been employed in the art to relate the time-averaged rate of mass flow and the time-averaged impact force received by the impact plate. However, example combine operations exhibit a strongly nonlinear dependence of the time-averaged impact force on the time-averaged rate of mass flow at larger flow rates. Nonlinear, model-based designs have been proposed in the art for mass flow sensors to account for changes in material properties.
A challenge to a practical implementation of such nonlinear models is the lack of knowledge of model parameters that characterize the grain behavior. Such model parameters include, as nonlimiting examples, effective coefficients of friction and restitution and their dependence on particle moisture levels. Similarly, mechanical aging of system components, such as (but not limited to) the elevator paddles 24, affects the values and physical interpretation of model parameters. Similar difficulties arise in the use of empirical models in which model coefficients lack physical origin.
As a result, repeated in-field calibration of a conventional mass-flow sensor system is typically required to reset model parameters for conditions of operation. Combine operators are often relied upon to initiate the calibration task. Currently, an adequate calibration of such a sensor relies on a combine operator making several (here at least three in order to calibrate three model parameters) passes of the combine across a section of a crop, recording the time-averaged sensor output during that time, and subsequently weighing the harvested crop during that time to determine the time-averaged mass flow rate. An ideal calibration procedure requires many more than three passes of the combine in order to achieve greater statistical strength in the measurements.
This example calibration procedure results in a relationship between the time-averaged output of the mass flow sensor and the time-averaged mass flow rate of the grain during the time interval of harvesting. This can be visualized as a 2-D graph with discrete points characterized by the sensor output (plotted on the x-axis) and the mass flow rate (plotted on the y-axis). Calibration is then accomplished by fitting a polynomial to these discrete data points in order to characterize the relationship between the time-averaged output of the sensor and the time-averaged mass flow rate.
The above-described procedure has several drawbacks. For example, the process is time-consuming and costly for the combine operator. Additionally, it is unlikely that combine operators will perform a multitude of combine passes to achieve several different collections of time-averaged mass flow rate and time-averaged sensor output values. Furthermore, this calibration procedure must be repeated each time there is a change in crop conditions (e.g., moisture, grain type, grain variety). This also relies on the ability of the combine operator to recognize these changes taking place and to subsequently take action to re-calibrate the system to remedy the effects of these changes.
Additionally, the passes of the combine that are made across crops to facilitate the calibration procedure should be made across sections of crops that will collectively yield a wide range of time-averaged mass flow rates (i.e., a distinct time-averaged mass flow rate for each pass that is representative of the instantaneous mass flow rates during the pass but that differs in value from the other passes made). If passes are made across crops that collectively yielded only a small range or cluster of time-averaged mass flow rates, the polynomial fit will be inadequate, will not have great statistical strength, and will not be representative of all operating conditions. This will likely lead to mass flow rate estimation errors when operating the combine over sections of crops that exhibit yields outside the range in which calibration was performed.
The calibration procedure only identifies model parameters that describe the relationship between the time-averaged sensor outputs and time-averaged mass flow rates across a significant duration of time, namely the entire time interval of harvesting across some section of crop, say on the order of tens of minutes. The extent to which this is applicable to the relationship between the time-averaged sensor outputs and time-averaged mass flow rates during much shorter sample periods depends on the statistical distribution of mass flow rates during the run. As such information is typically not available, the calibrated model is instead assumed to be valid also across the sample periods typical of the mass-flow sensing system during real-time operation, say on the order of seconds.